Course Information

In this advanced course we'll be investigating hot topics in data mining that the lecturers think are cool. This course is for those of you who are interested in Big Data Analytics, Data Science, Data Mining, Machine Learning – or, as the lecturers prefer to call it – Algorithmic Data Analysis. We'll be looking into how to discover significant and useful patterns from data, efficiently measure non-linear correlations and determine causal directions, as well as how to analyse large graphs and multi-linear structures.

Materials

All required and optional reading will be made available. You will need a username and password to access the papers outside the MPI network. Contact the lecturers if you don't know the username or password.

Course format

The course has two hours of lectures per week. There are no weekly tutorial group meetings. Instead, the students have to write essays based on the material covered on the lectures and scientific articles assigned to them by the lecturers.

Structure and Content

In general terms, the course will consist of

lectures, and

assignments that include critically reading scientific articles

At a high level, the topics we will cover will include

Mining Significant Patterns

Mining Correlation and Causation

Mining Graphs and Tensors

Loosely speaking, students will learn about current hot topics in exploratory data analysis, with an emphasis on theoretically well-founded approaches, including those based on information theoretic principles.

Assignments

Students will individually do one assignment per topic – four in total. For every assignment, you will have to read one or more research papers and hand in a report that critically discusses this material and answers the assignment questions. Reports should summarise the key aspects, but more importantly, should include original and critical thought that show you have acquired a meta level understanding of the topic – plain summaries will not suffice. All sources you've drawn from should be referenced. The expected length of a report is 3 pages, but there is no limit.

The deadlines for the reports are at 10:00 Saarbrücken standard-time. You are free to hand in earlier.

Grading and Exam

The assignments will be graded in scale of Fail, Pass, Good, and Excellent.
Any assignment not handed in by the deadline is automatically considered failed, and cannot be re-done.
You are allowed to re-do a Failed assignment once: you have to hand in the improved assignment within two weeks.
Two failures mean you fail the course.

You can earn up to three bonus points by obtaining Excellent or Good grades for the assignments.
An Excellent grade gives you one bonus point, as do every two Good grades, up to a maximum of three bonus points.
Each bonus point improves your final grade by 1/3 assuming you pass the final exam.
For example, if you have two bonus points and you receive 2.0 from the final exam, your final grade will be 1.3.
You fail the course if you fail the final exam, irrespective of your possible bonus points.
Failed assignments do not reduce your final grade, provided you are eligible to sit the final exam.

The final exams will be oral.
The final exam will cover all the material discussed in the lectures and the topics on which you did your assignments.
The main exam will be on July 25th and 26th.
The re-exam will be on September 30th.
The exact time slot per student will be announced later.
Inform the lecturers of any potential clashes as soon as you know them.

Prerequisites

Students should have basic working knowledge of data analysis and statistics, e.g. by successfully having taken courses related to data mining, machine learning, and/or statistics, such as
Information Retrieval and Data Mining,
Machine Learning,
Probabilistic Graphical Models,
Statistical Learning,
etc.